Arabian Journal for Science and Engineering最新文献

The escalating demand of drinking water, coupled with increasing agricultural and industrial needs, has imposed a significant strain on groundwater sources worldwide, which is further exacerbated by the implications of climate change. The current research aims to identify the groundwater potential zones (GPZs) and the optimal sites for artificial recharge in the Wadi Waj watershed, Taif, Saudi Arabia. An integrated approach using remote sensing (RS), GIS, multi-criteria decision analysis (MCDA), and the analytic hierarchy process (AHP) was utilized. Seven hydrological and geological factors have been weighted and analyzed using AHP and GIS-based weight overlay approaches. Five GPZs have been mapped with the range from very high to very low where the very high, high, moderate, low, and very low GPZs were 0.046, 33.5, 31.3, 35.1, and 0.023 percent of the basin, respectively. The high GPZs were found in the downstream region, while the low GPZs were located in the upstream region with impermeable rock and steep slopes. A total of 27 locations have been identified as optimal sites for constructing artificial recharge facilities. These sites are positioned strategically to both catch and encourage the infiltration of rainwater into the ground. The findings were verified with the agricultural farms and wells demonstrating the alignment with moderate to high GPZs. The GPZs were also verified with the groundwater depth data obtained from the global model “GLOBGM v1.0,” and the findings showed comparable trends. The findings will support water resources management to enhance regional water security and maintain sustainable groundwater resources.

The goal of the IAEA Coordinated Research Project “Benchmark of Transition from Forced to Natural Circulation Experiment with Heavy Liquid Metal Loop” (CRP—I31038) is to develop Member State advanced fast reactor analytical capabilities for simulation and design using system, CFD, and subchannel analysis codes. Here, CFD validation employing the commercial CFD code Star CCM + applied to the fuel pin simulator for forced and natural convection cases in the open phase is presented. Experimental data are provided in the benchmark specification provided by ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) for the NACIE-UP facility (NAtural CIrculation Experiment-UPgrade). Considered is the fuel pin simulator with 19 pins, each consisting of a preheated lower section and heated upper sections, respectively. Three configurations: (i) all pins heated, (ii) inner 7 pins heated and (iii) asymmetric heating, are studied. For each heating configuration data for forced and natural convection are provided. Here, case (i) is studied. Temperatures at three planes are measured near the inlet, in the middle and near the end of the heated section, respectively. In addition, the axial temperature along the wall of one fuel pin simulator (in second row) is measured so that in total 67 thermocouples measure fluid and wall temperatures for validation purposes. The validation confirms that the thermohydraulic inside the fuel pin simulator can be simulated with a good accuracy. Applied is a polyhedral mesh with 2 prism layers, the k-omega SST model with all all-wall treatment and order unity y + values. Moreover, a grid-sensitivity, the importance of conjugate heat transfer inside the fuel pin simulators and the wrapper are studied. The studies indicate that it is possible to implement further simplifications without corrupting the accuracy of the simulation to reduce computational effort.

An essential factor influencing photovoltaic (PV) panel performance is its operating temperature. Various active and passive cooling methods have been explored in the literature to mitigate the effects of high operating temperatures; however, recent research has shown a growing interest in hybrid cooling systems that combine both active and passive approaches. In this context, phase change material (PCM) serves as a passive cooling method, while fluid is employed as an active cooling medium. This study introduces a channel into the PV panel base through which fluid flows. Additionally, a PCM layer is placed at the bottom of the water channel to reduce the average temperature of the fluid, thus extracting more heat compared to direct contact with the PV panel. The proposed model is compared with traditional water-cooled PV panels using a parametric approach, with varying parameters including concentration ratio, environmental temperature, wind speed, mass flow rate of water in the channel, and inlet temperature. The study findings reveal that the proposed model leads to an increase in electricity production within the range of 1.4–7 kW, an improvement in PV efficiency between 1.6 and 3.8%.

The task of deducing directed acyclic graphs from observational data has gained significant attention recently due to its broad applicability. Consequently, connecting the log-det characterization domain with the set of M-matrices defined over the cone of positive definite matrices has emerged as a crucial approach in this field. However, experimentally collected data often deviates from the expected positive semidefinite structure due to introduced noise, posing a challenge in maintaining its physical structure. In this paper, we address this challenge by proposing four methods to reconstruct the initial matrix while maintaining its physical structure. Leveraging advanced techniques, including sequential quadratic programming (SQP), we minimize the impact of noise, ensuring the recovery of the reconstructed matrix. We provide a rigorous proof of convergence for the SQP method, highlighting its effectiveness in achieving reliable reconstructions. Through comparative numerical analyses, we demonstrate the effectiveness of our methods in preserving the original structure of the initial matrix, even in the presence of noise.

This paper explores the innovative application of machine learning and neural network algorithms to predict pore types in carbonate rocks using experimental acoustic properties under ambient pressure conditions. Carbonate reservoirs, crucial for hydrocarbon storage and extraction, present a challenge due to their complex pore structures influenced by diverse depositional environments and diagenetic processes. Traditional petrographic methods for identifying pore types, though accurate, are time-consuming and destructive. Recent approaches leverage log and core-measured compressional wave velocities and porosity, yet variability in data remains an issue. Addressing the challenge, this study distinguishes itself by employing high-resolution physical rock samples from the early Miocene dam formation, eastern province of Saudi Arabia. Through meticulous data preparation, feature engineering, and the evaluation of logistic regression, random forest classifier, gradient boosting classifier, and support vector classifier models, we have developed an advanced model capable of predicting pore types with significant accuracy. Our findings reveal that logistic regression achieves the highest accuracy (71%) among the models, effectively capturing the inherent patterns within our dataset. A detailed analysis using principal component analysis underscored the discriminative power of these models, particularly in identifying interparticle–intraparticle and moldic pore types. This study’s innovative approach, leveraging experimental measurements and machine learning techniques, offers a robust framework for accurately predicting pore types in carbonate rocks. While challenges such as data size and feature limitations persist, the potential implications of our findings for reservoir modeling and efficient hydrocarbon extraction are significant, providing a foundation for future research to build upon.

In this study, we conducted a comparative quantum computational investigation about carbazole/borole derivatives to understand how different configurations like linear and bent, and terminal groups can affect their linear and second hyperpolarizability properties. The goal was to compare the optical and NLO response properties, photovoltaic parameters and charge transfer properties of these linear/bent configurations. Among all the designed compounds the linear compounds exhibited larger linear isotropic and anisotropic polarizability and second hyperpolarizability amplitudes (γ) compared to the bent compounds. The highest values of isotropic polarizability of Py-1L and Py-2L are calculated to be 109.0 × 10^–24 esu and 103.9 × 10^–24 esu, respectively. Notably, linear configurations Py-1L and Py-2L achieved the (leftlangle gamma rightrangle) amplitudes as high as 840.1 × 10⁻³⁶ esu and 776.9 × 10⁻³⁶ esu. When compared to the prototype para-nitroaniline (p-NA) molecule, these amplitudes are found to be ~ 115 times and ~ 113 times larger than p-NA as calculated at the same level of theory. Moreover, TD-DFT calculations also revealed that linear configuration gave better NLO response due to their higher oscillator strengths, dipole moment changes between ground and excited states and lower transition energy values among all the designed compounds. Frontier molecular orbitals, molecular electrostatic potential map, electron density difference and natural bond orbitals analysis indicated that more efficient intramolecular charge transfer in linear configuration, leading to high NLO response than bent configuration. The highest light harvesting efficiencies have been exhibited by Py-1L and Py-2L, with values of 0.928 eV and 0.903 eV, respectively. Overall, the current systematic comparison of NLO polarizabilities and other electronic properties emphasized the importance of configuration-based designs for achieving high performance NLO response properties in the designed compounds.

Supercritical fluids are used as coolants in one of the Generation-IV reactors (i.e., supercritical water reactor) owing to their good diffusivity, low viscosity, and high specific heat. Additionally, these fluids exist at higher pressure and temperature which allows high thermal efficiency. Two heat transfer phenomena are related to supercritical fluids: heat transfer deterioration and enhancement. These phenomena made it difficult for Reynolds-averaged Navier–Stokes simulation (RANS)-based turbulence models to accurately predict the heat transfer. In this study, an assessment of RANS-based turbulence models is conducted for supercritical carbon dioxide under two different flow conditions (i.e., horizontal flow and natural circulation vertical flow). The two cases are simulated using current turbulence models (i.e., SST k-ω, k-ε, RNG k-ε) and a newly developed model based on the algebraic heat flux model (AHFM), hereafter called UniPi. It was found that for the horizontal flow case, the SST k-ω model captured the temperature difference induced by buoyancy between different regions of the wall, however, with poor accuracy in predicting wall temperatures. The RNG k-ε models captured the behavior of wall temperature across all regions with underestimated values. The enhanced wall treatment gives good predictions of wall temperatures compared to experimental data, but it underestimates the deterioration and recovery of heat transfer. In the natural circulation case, the recently developed model, which is based on AHFM, yielded better results compared to k-ε and the SST k-ω models. This is mainly because it explicitly considers the buoyancy production term and the turbulent heat flux.

This paper is focused on a comprehensive review related to the applications of machine learning (ML) and deep learning (DL) techniques for corrosion and crack detection in nuclear power plants (NPPs). NPPs require strict inspection and maintenance guidelines to ensure safety and efficiency, as the consequence of any such accident can be disastrous. Traditional methods of corrosion and crack detection often require substantial manual effort, even plant shutdown for inspection, and are limited in scalability. In recent years, ML and DL approaches have appeared as promising solutions to improve the accuracy and efficiency of corrosion and crack detection methods. The review begins by exploring the fundamental principles of ML and DL, providing insights into their adaptability for managing these challenges in NPPs. ML techniques such as support vector machines and decision trees (DT) as well as various DL architectures, including convolutional neural networks, recurrent neural networks, and autoencoders, are explored in the context of corrosion and crack detection. The paper highlights the dataset challenges related to NPPs, handling issues like imbalanced data, temporal dependencies, and multi-scale modeling. It focuses on case studies and research efforts utilizing ML techniques, highlighting notable advancements and potential breakthroughs in the field. Further, the challenges and future opportunities of integrating ML techniques into nuclear power plant inspection and maintenance are thoroughly scrutinized, underscoring the imperative need for standardized datasets, scalability, and model interpretability.

The impact of the climate and environmental problems experienced in the world with the Industrial Revolution has prominently begun to be felt today, and the consequences of climate change on the environment and public health have now become visible. The increase in greenhouse gas emissions resulting from human activities, which is the main cause of global climate change, caused the global surface temperature to be 1.1 °C higher between 2011 and 2020 compared to 1850–1900. In parallel with this global problem, the transition to clean energy has increased significantly with Russia's invasion of Ukraine, more aggressive energy and climate policies, technological developments, and increasing concerns about energy security. In this study, global climate change indicators, including land and sea surface air temperatures, sea level rise, sea ice extent, ocean heat content, surface humidity, and total column water vapor, are reviewed and updated in parallel with a comprehensive analysis of the progress in renewable energy. The results showed that if no measures are taken to reduce human-induced greenhouse gas emissions, the global average temperature will increase further in the coming years and the negative effects of other climate parameters will be felt even more. It has been emphasized that limiting human-induced global warming requires renewable and sustainable energy sources and net zero CO₂ emissions and that the simultaneous adoption of emission reduction and adaptation strategies will be the most effective economic and technical solution to the global warming problem.

This paper outlines the synthesis and characterization of semiconducting films composed of 0–2.0% Al-doped SnO₂, tailored for gas sensor applications in aerospace contexts. The films were fabricated via the sol–gel method on glass substrates. Transparent solutions were prepared from Al and Sn salt precursors, methanol, and glacial acetic acid. Solution characteristics, such as pH and rheological properties, were evaluated prior to the coating process. Gel coatings were dried at 300 °C for 10 min and annealed at 600 °C for 1 h in air. Structural, microstructural, and optical properties were analyzed using Fourier transform infrared spectrophotometer, X-ray diffraction, scanning electron microscopy, refractometer, and UV/VIS spectrophotometer. The study revealed that solution properties influenced film structure and microstructure, with acidic conditions affecting hydrolysis, condensation, and gelation, and higher viscosities resulting in thicker films. SnO₂ formation occurred between 410 and 500 °C, with a preferential (110) texture observed after annealing. Incorporating Al altered film morphology and microstructure, reducing microcrack formation and introducing nano-sized particles, thereby enhancing film quality and structural integrity. Refractive index, film thickness, and energy range of the Al–SnO2 films met requirements for gas sensor production. Gas sensitivity tests showed approximately 53% sensitivity to CO₂ at room temperature. The findings suggest that Al-doped SnO₂ films exhibit promising characteristics for aerospace gas sensing applications.